Creating a Basic Interactive Dashboard with hvPlot and Panel
Overview
Interactive visualuzation is …
By the end of this notebook, you should be able to:
Understand the necessecity for interactive plots and the challenges associated with them
Use
hvPlotto generate basic interactive plots withXarrayCreate an interactive dashboard for visualuzing geoscience datasets using
PanelandhvPlot
Prerequisites
Concepts |
Importance |
Notes |
|---|---|---|
Necessary |
Time to learn: 30 minutes
Imports
import xarray as xr
import panel as pn
import holoviews as hv
from holoviews import opts
hv.extension("bokeh")
Data
As was discussed in the Data Aquisition …
rda_url = 'https://data.rda.ucar.edu/'
annual_means = rda_url + 'pythia_era5_24/annual_means/'
xrds = xr.open_dataset(annual_means + "temp_2m_annual_1940_2023.zarr", engine= 'zarr')
xrds.load()
xrds['VAR_2T_ANOM_FROM_1940'] = xrds['VAR_2T'] - xrds['VAR_2T'][0]
xrds
<xarray.Dataset> Size: 698MB
Dimensions: (time: 84, latitude: 721, longitude: 1440)
Coordinates:
* latitude (latitude) float64 6kB 90.0 89.75 ... -89.75 -90.0
* longitude (longitude) float64 12kB 0.0 0.25 0.5 ... 359.5 359.8
* time (time) datetime64[ns] 672B 1940-12-31 ... 2023-12-31
Data variables:
VAR_2T (time, latitude, longitude) float32 349MB 258.2 .....
VAR_2T_ANOM_FROM_1940 (time, latitude, longitude) float32 349MB 0.0 ... ...Considerations for Interactive Plots
Add some markdown text on some of the following ideas:
What are some reasons we want to make data visualuzation interactive?
Baisc Interactivity using hvPlot
The hvPlot package is a familiar and high level API for data exploration and visualuzation.
One of the most powerfull features of hvPlot is that it provides an alternative plotting API that directly attaches to existing Python objects through the .hvplot() attribute. For the case of Xarray, importing hvplot.xarray adds a brand new set of plotting routines accessible either through xr.DataArray.hvplot() or xr.Dataset.hvplot()
import hvplot.xarray
Before using hvPlot, let’s take a look at the default Xarray plotting methods.
xrds['VAR_2T'].plot()
(array([ 1038607., 3420082., 1676489., 2612838., 9834305., 9283152.,
12940102., 12676054., 22733444., 10997087.]),
array([215.00773621, 224.33287048, 233.65800476, 242.98313904,
252.30827332, 261.63339233, 270.95852661, 280.28366089,
289.60879517, 298.93392944, 308.25906372]),
<BarContainer object of 10 artists>)
We can replace the .plot() function call with .hvplot(). By default, hvPlot uses the Bokeh backend, which has naitive interactive tools, such as :
Panning
Box Select
Scroll Zoom
Saving
Resetting
xrds['VAR_2T'].hvplot()
If we wanted to plot …
xrds['VAR_2T'].isel(time=0).plot()
<matplotlib.collections.QuadMesh at 0x7f3010553a90>
Switching
xrds['VAR_2T'].isel(time=0).hvplot()
Time Widget
Climate data typically comes with multiple timesteps. We can create a basic widget that allows us to seek through time by setting the groupby='time' parameter in our .hvplot() call.
xrds['VAR_2T'].hvplot(groupby='time', widget_location="bottom")
You may notice that our colorbar is dynamically changing as we change our time steps. We can fix the colorbar by setting a clim value, which is a tuple of the minimum and maximum desired colorbar range.
One suggestion is to use the minimum and maximum of the data variable you are visualuzing across time.
clim = (xrds['VAR_2T'].values.min(), xrds['VAR_2T'].values.max())
xrds['VAR_2T'].hvplot(clim=clim, groupby='time', widget_location="bottom")
You may have noticed that there is a slight lag when switching time steps. This is due to hvPlot plotting the full resolution of our dataset. We can instead rasterize the output by setting rasterize=True, which will significantly improve the perfromance of our interactive plot.
xrds['VAR_2T'].hvplot(rasterize=True, clim=clim, groupby='time', widget_location="bottom")
Animation Widget
Another usefull interactive feature is animations. Instead of manually scrolling through time, we can set up a widget that lets us animate our data across time. This can be achieved by adding a Scrubber widget to our plot by setting widget_type="scrubber"
xrds['VAR_2T'].hvplot(
rasterize=True,
groupby="time",
widget_type="scrubber",
widget_location="bottom",
)
Creating a Dashboard
Dataset Widgets
w_time = pn.widgets.IntSlider(name='Year', start=0, end=83)
w_var = pn.widgets.Select(name='Data Variable', options=list(xrds.data_vars))
dataset_controls = pn.WidgetBox(
'## Dataset Controls',
w_var,
)
dataset_controls
Plotting Widgets
w_cmap = pn.widgets.Select(name='Colormap', options=['inferno', 'plasma', 'coolwarm'])
w_plot_type = pn.widgets.Select(name='Plot Type', options=['Color Plot', 'Contour', 'Filled Contour'])
plot_controls = pn.WidgetBox(
'## Plot Controls',
w_plot_type,
w_cmap,
)
plot_controls
Animation Widgets
w_player = pn.widgets.Player(
value=0,
start=0,
end=83,
name="Year",
loop_policy="loop",
interval=300,
align="center",
width_policy='fit'
)
w_player
Plotting Function
def plot_ds(time, var, cmap, plot_type):
clim = (xrds[var].values.min(), xrds[var].values.max())
if plot_type == "Color Plot":
return xrds[var].isel(time=time).hvplot(cmap=cmap,
title=str(f"{var} year {time}"),
clim=clim,
dynamic=False,
rasterize=True,
precompute=True,
).opts(framewise=False)
elif plot_type == "Contour":
return xrds[var].isel(time=time).hvplot.contour(cmap=cmap,
dynamic=False,
rasterize=True,
title=str(f"{var} Year: {time}"),
clim=clim,
precompute=True,).opts(framewise=False)
elif plot_type == "Filled Contour":
return xrds[var].isel(time=time).hvplot.contourf(cmap=cmap,
dynamic=False,
rasterize=True,
title=str(f"{var} Year: {time}"),
clim=(200, 300),
precompute=True,).opts(framewise=False)
Putting it all Together
controls = pn.Column(dataset_controls, plot_controls)
app = pn.Row(
controls,
pn.Column(pn.panel(
hv.DynamicMap(pn.bind(
plot_ds,
time=w_player,
var=w_var,
cmap=w_cmap,
plot_type = w_plot_type
)
)
),
w_player)
)
app